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Bayesian network-based high-speed rail traction system fault prediction method

A technology of Bayesian network and traction system, applied in the field of failure prediction of high-speed rail traction system based on Bayesian network, can solve problems such as open-loop system instability, reduce operation difficulty and maintenance cost, and achieve high practicability and reliability Sexuality and accurate results

Active Publication Date: 2019-04-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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AI Technical Summary

Problems solved by technology

At present, the bond graph model of the open-loop system has been built, but the traction system will be disturbed by many environmental factors in the actual operation process, and the open-loop system will become extremely unstable, so the closed-loop control link in the actual system is essential of

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  • Bayesian network-based high-speed rail traction system fault prediction method
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  • Bayesian network-based high-speed rail traction system fault prediction method

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Embodiment Construction

[0047] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. The embodiments described in the present invention are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, other embodiments obtained by persons of ordinary skill in the art without creative efforts all belong to the protection scope of the present invention.

[0048] refer to figure 1 Shown, a kind of high-speed rail traction system fault prediction method based on Bayesian network of the present invention, first set up the closed-loop high-speed rail traction system bonding graph model, build Bayesian network structure according to the causal relationship provided by the bonding graph model, and then Perform deep feature extraction on the original signal, and use k-means to divide it into three categories: long-term, medium-term, and short-term according to the remainin...

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Abstract

The invention discloses a Bayesian network-based high-speed rail traction system fault prediction method. The method comprises the following steps of: 1, establishing a closed-loop bonding graph model: adding a direct torque control module into an original bonding graph model; 2, building a Bayesian network: firstly determining nodes of a Bayesian network structure, determining the structure of the Bayesian network according to the causal relationship between the nodes provided by the closed-loop bonding graph model, and finally opening a closed loop in the Bayesian network; 3, data preprocessing: filtering and denoising the original signal, extracting features, monotonically screening and extracting deep features, and finally, carrying out k-feature extraction on the features; performingmeans clustering; and 4, fault prediction: carrying out supervised learning by using an EM algorithm, obtaining the state of an online measured value by using the data preprocessing method, and carrying out classification by using a Bayesian network to obtain the residual life range of the equipment. Equipment fault prediction can be realized, and the method is more practical and reliable.

Description

technical field [0001] The invention belongs to the technical field of equipment fault prediction, in particular to a Bayesian network-based high-speed rail traction system fault prediction method. Background technique [0002] Fault prediction starts from the current state of use of the equipment, and combines the structural characteristics, parameters, environmental conditions and historical data of the known prediction object to predict, analyze and judge the future faults of the equipment. Fault prediction has great uncertainty, because the object failure mechanism itself is a random process, and the prediction process itself will also produce errors. Due to the large operating range of my country's high-speed rail, the complex and changeable climatic conditions increase the complexity of its working environment, further increase the uncertainty of fault prediction, and bring great challenges to fault prediction. [0003] Bayesian network is currently one of the most ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06Q10/04
CPCG06Q10/04G06F30/20
Inventor 张传宇朱凯强陆宁云姜斌
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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